AI-Native Delivery Services

Build clearer requirements and reduce rework later. AI agents can analyse approved project information, draft requirements and check their consistency within a connected workflow. Sii analysts guide the process, resolve open questions with stakeholders and approve the results.

Agents analyse stakeholder input, existing requirements and project documents to identify business rules and prepare requirement drafts and acceptance criteria. Separate review agents check for gaps, conflicts and unsupported assumptions, and return incomplete or inconsistent work for correction. Analysts validate the findings with stakeholders and approve the requirements. Approved requirements, decisions and assumptions then provide a shared basis for architecture, development and testing.

Turn approved requirements into a clear design for implementation. AI agents can develop and compare design options, prepare technical designs and check their consistency. Sii architects guide the work, assess trade-offs and make the final architecture decisions.

Agents use approved requirements, business rules and project constraints to analyse dependencies and propose system components, interfaces and data flows. They draft technical designs and document options, assumptions and risks. Separate review agents check the proposals against requirements and agreed security, performance and integration criteria, returning inconsistent or incomplete work for correction. Architects review the proposals, resolve trade-offs and approve the design, recording the reasons for key decisions. The approved design, interface definitions and data model guide development and test design.

Deliver software faster through coordinated work between engineers and specialised AI agents. Agents can implement changes, run tests and address failures within a defined workflow. Sii engineers set the scope, review the results and approve changes, helping the team complete more work with less repetitive effort.

Agents work from approved requirements, technical designs and project instructions. A coordinating workflow assigns coding, testing and documentation tasks, running independent tasks in parallel where appropriate. Command-line tools (CLI) enable agents to carry out connected build and test steps. Approved MCP connections give them access to project tools within defined permissions.

Separate review agents check the code against requirements and the approved design. Automated quality gates run builds, tests, regression checks and security scans. Failed checks trigger correction or escalation to an engineer. Agents work on controlled branches, and engineers approve changes before merge. Data access and tool use follow project rules, with actions logged for review. We monitor delivery speed, review effort and rework to assess productivity gains.

Scale automated testing to keep pace with faster development. Sii quality engineers coordinate AI agents to design, build, run and maintain tests, using tools such as Playwright for web applications. This helps teams check more changes sooner and focus engineering effort on important quality risks.

Agents work from approved requirements, business rules and technical designs, with expected results validated by engineers. They create test scenarios and automation, execute tests and analyse failures. Separate agents review test quality and coverage, while engineers confirm defects and approve changes to the tests.

Repeatable checks run automatically in CI/CD pipelines, with quality gates flagging regressions and unmet criteria. Approved CLI tools, MCP connections, controlled permissions and action logs govern agent activity. Engineers combine automated results with exploratory testing to assess remaining risks and provide evidence for the authorised release decision.

Deploy faster and reduce release risk. Sii engineers use AI agents to prepare release changes, run checks and plan how to restore the previous version if needed. They verify test and security results before an authorised person approves deployment, reducing manual preparation while keeping the release decision with the responsible person.

Agents can update deployment scripts, assemble release notes and run checks in approved environments. Engineers review the proposed changes and confirm that testing, security and recovery steps meet the release criteria. The team documents any remaining issues before the release decision. Deployment follows your established process, with an authorised person deciding whether the change can go live.

Resolve issues faster and keep services stable. Sii specialists use AI to find relevant service information, investigate issues and prepare changes. They check the findings against your operating procedures before taking action, helping teams diagnose issues faster without putting service stability at risk.

After an incident, AI also helps turn the findings into updated documentation, knowledge-base content and regression tests, so teams can use what they learned the next time a similar issue occurs.